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Publications Influence

Greedy function approximation: A gradient boosting machine.

- J. Friedman
- Mathematics
- 1 October 2001

Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions… Expand

9,698 1208- PDF

The Elements of Statistical Learning

- T. Hastie, R. Tibshirani, J. Friedman
- Computer Science
- 2001

13,034 1051- PDF

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition

- T. Hastie, R. Tibshirani, J. Friedman
- Mathematics, Computer Science
- Springer Series in Statistics
- 1 March 2005

TLDR

13,459 963- PDF

Regularization Paths for Generalized Linear Models via Coordinate Descent.

- J. Friedman, T. Hastie, R. Tibshirani
- Computer Science, Medicine
- Journal of statistical software
- 2 February 2010

TLDR

8,625 682- PDF

Sparse inverse covariance estimation with the graphical lasso.

- J. Friedman, T. Hastie, R. Tibshirani
- Mathematics, Medicine
- Biostatistics
- 1 July 2008

We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the… Expand

3,819 545- PDF

Stochastic gradient boosting

- J. Friedman
- Mathematics
- 28 February 2002

Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration. The… Expand

3,286 332- PDF

Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By

The main and important contribution of this paper is in establishing a connection between boosting, a newcomer to the statistics scene, and additive models. One of the main properties of boosting… Expand

1,622 327

Regularized Discriminant Analysis

- J. Friedman
- Mathematics
- 1 March 1989

Abstract Linear and quadratic discriminant analysis are considered in the small-sample, high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance… Expand

2,183 259- PDF

Special Invited Paper-Additive logistic regression: A statistical view of boosting

- J. Friedman
- Mathematics
- 1 April 2000

Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data… Expand

4,075 257- PDF

PATHWISE COORDINATE OPTIMIZATION

- J. Friedman, T. Hastie, Holger Hofling, R. Tibshirani
- Mathematics
- 10 August 2007

We consider “one-at-a-time” coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the L1-penalized regression (lasso) in the… Expand

1,761 191- PDF